CHESS

The next CHESS meeting will be tomorrow, Monday January 23, from 10:00 to 11:30 (French time). It will be in room Chartreuse, and with Skype connection with the team of Prof. Babaie-Zadeh in Tehran.

There will be two talks, the first given by Fateme GHAYEM and the second by Saloua CHLAILY.
Fateme GHAYEM
Title: Sparse Signal Recovery via Iterative Sparsification-Projection: A Closer Look and Accelerated Extensions

Abstract: This paper studies a recently proposed family of algorithms, called iterative sparsification-projection (ISP), for
recovery of sparse signals. The ISP algorithms are motivated by the idea of the smoothed `0 (SL0) method, which approximates the L0 norm with a continuous and differentiable function and solves an error-constrained problem by utilizing the well-known graduated non-convexity scheme. The ISP algorithms generalize this idea to non-smooth sparsity inducing functions. Although these algorithms have shown promising performance, some important aspects of them have not yet been fully investigated. In particular, there is no direct derivation and convergence analysis for the non-smooth case. In this paper, we are going to demonstrate the potential advantages of the ISP algorithms by revealing some interesting properties of them along with proposing acceleration schemes to further enhance their recovery performance. More precisely, it is shown that the SL0 shrinkage is in fact a smooth interpolation between hard and soft thresholdings.
Furthermore, the close connection of the SL0 shrinkage with a well-known shrinkage function, called smoothly clipped absolute deviation (SCAD), is discussed. Our simulations indicate that using the SCAD shrinkage in ISP leads to a significant improvement of the performance relative to the use of other shrinkage functions. As another contribution, using an alternating minimization penalty method, we directly derive the ISP algorithms for non-smooth sparsity-promoting functions, including SCAD and hard thresholding. Moreover, we propose accelerated extensions of the ISP algorithms for
both smooth and non-smooth cases, and establish convergence to critical points for the resulting algorithms. Our extensive simulations verify that the new accelerated algorithms considerably outperform their plain versions and some wellknown and recently proposed algorithms.

Saloua CHLAILY
Title: Information-Estimation relationship in Mismatched Gaussian channels
Abstract: In this paper, we investigated the connection between information and estimation measures in mismatch modeling contexts. Additionally to the input prior mismatch, the novelty of this paper is to take into account the noise mismatch which has not been studied yet and deserves to be explored for some applications. A new relation between relative entropy and excess mean square error is stated. Finally, an example illustrates the impact of model mismatches on estimation accuracy.